A liquid crystal tunable filter based shortwave infrared spectral imaging system: Design and integration

This paper presents the methodology to design and integrate a liquid crystal tunable filter (LCTF) based shortwave infrared (SWIR) spectral imaging system. The system consisted of an LCTF-based SWIR spectral imager, an illumination unit, a frame grabber, and a computer with the data acquisition software. The spectral imager included an InGaAs camera (320 × 256 pixels), an SWIR lens (50 mm, F/1.4), and an LCTF (20 mm aperture). Four multifaceted reflector halogen lamps (35 W, 12 VDC) were used to build the illumination unit. The system was integrated by a LabVIEW program for data acquisition. It can capture hyperspectral or multispectral images of the test object in the spectral range of 900–1700 nm. The system was validated by differentiating sugar from wheat flour, and water from 95% ethanol. The results showed that the system can distinguish these materials in both spectral and spatial domains. This SWIR spectral imaging system could be a potential useful tool for nondestructive inspection of food quality and safety.     

A high-resolution airborne four-camera imaging system for agricultural remote sensing

This paper describes the design and testing of an airborne multispectral digital imaging system for remote sensing applications. The system consists of four high resolution charge coupled device (CCD) digital cameras and a ruggedized PC equipped with a frame grabber and image acquisition software. The cameras are sensitive in the 400 to 1000 nm spectral range and provide 2048 × 2048 active pixels with 12-bit data depth. A 24 mm lens is attached to each camera via an F to C mount adapter, resulting in an imaging size of 0.63 times the flight altitude. The four cameras are equipped with blue (430–470 nm), green (530–570 nm), red (630–670 nm), and near-infrared (NIR) (810–850 nm) bandpass interference filters, respectively, but have the flexibility to change filters for desired wavelengths and bandwidths. The cameras are arranged in a quad configuration and attached to adjustable mounts that facilitate aligning the cameras horizontally, vertically, and rotationally. The image acquisition software allows the synchronized black-and-white band images from the cameras to be viewed on the computer monitor in any one of the four modes: a quad, one band image at a time, a normal color composite, or a color-infrared (CIR) composite. The band images are refreshed continuously to allow the operator to selectively save images with correct areas of interest. The selected four-band composite image is saved as a tiff file and consecutive images can be saved in 1-s intervals. A band-to-band alignment procedure based on the first- and second-order polynomial transformations was presented to further align the four band images. The system performed well in both stationary and airborne testing conditions. Airborne images obtained from agricultural fields, rangelands, and waterways demonstrate that this system has potential for monitoring crop pest conditions, mapping invasive weeds and assessing natural resources.     

Comparison of segmentation algorithms for cow contour extraction from natural barn background in side view images

Computer vision techniques are a means to extract individual animal information such as weight, activity and calving time in intensive farming. Automatic detection requires adequate image pre-processing such as segmentation to precisely distinguish the animal from its background. For some analyses such as gait analysis, a side view perspective is recommended. When using a side view angle however, the background is more difficult to control – moving objects, such as other animals may negatively impact successful image segmentation. The objective of this research was to evaluate five different background segmentation algorithms on side view images when taken against a static background (a solid transportable wall) and a dynamic background (open air, without a wall).The experiments were conducted on a commercial robotic-milking dairy farm in Israel with a herd size of 70 Israeli Holstein cows. A side view image of cow’s gait was recorded after milking when the cows exited the milking area and returned to the cowshed. From the recording database, a random selection was made of 35 frames containing a static background (solid wall) and 20 frames containing a dynamic background (natural barn environment with other cows).Five segmentation algorithms were chosen and adapted from literature to extract the cow shape from the image. The output of three algorithms gave the cow’s full body shape two identified only the contour of the cow’s body. The algorithms were compared on their ability to correctly identify the cow’s back contour line. The performance of each algorithm was quantified by comparing its outputs to a golden standard of manually labelled cow pixels in the image.The introduction of a physical wall behind the cows (static background) significantly improved the foreground segmentation results (Mean Absolute Error (MAE) = 6.7 ± 5.7 pixels vs. 19.7 ± 9.1 pixels). The fourth algorithm, based on an edge detection on the background difference frame, gave the best cow back contour line segmentation results (b₀ = −0.4 ± 15.5 and b₁ = 1.00 ± 0.07). The fifth algorithm which is based on consecutive frame differences was less accurate than the other four methods which are based on the background frame differences (MAE = 16.0 ± 5.9 pixels vs. 4.1 ± 2.2 pixels, 4.3 ± 2.2 pixels, 5.6 ± 2.8 pixels and 3.7 ± 1.4 pixels respectively for the other four algorithms). The results show that the applied algorithms were not robust enough to work on side view images with dynamic backgrounds.     

Development of software for spectral imaging data acquisition using LabVIEW

Developing data acquisition software is a major challenge in integrating a spectral imaging system. This paper presents the design and implementation of a data acquisition program using LabVIEW for a liquid crystal tunable filter based spectral imaging system (900–1700 nm). The module-based program was designed in a three-tier structure. The image acquisition process, modelled by a finite state machine, was implemented in LabVIEW to control the spectral imaging system to collect hyperspectral or multispectral images. The collected spectral images were encoded in general format and could be further processed by other common spectral image analysis tools. In addition, the program could be used to observe band ratio images of the test object in real-time, collect spectral images after ensemble averaging, and select region of interest for spectral image acquisitions. This program is a useful data acquisition tool for the filter-based spectral imaging system. The design and implementation techniques described in this article could also be used to develop similar spectral image acquisition programs.     

The development and application of an electrical impedance spectroscopy measurement system for plant tissues

For the impedance measurement system for plant tissues, a stable current source, the magnitude of which may be affected by the capacity of the operational amplifier, loading and high-frequency noise. Hence, this application note describes the use of a second-generation current conveyor (CCII) to improve the electrical circuit, plus a voltage buffer is used to significantly reduce the loading effect. A personal computer is integrated with LabVIEW software for data collection, analysis and processing. This forms a highly-effective measurement system which improves the portability of the measurement instrument. Within the frequency range 100 Hz–1 MHz, two groups of precision resistance-capacitance are adopted: 49.98 kΩ, 1.01 nF; 49.98 kΩ, 9.8 nF to execute the measurement simulation. The results of simulation measurement show that all the errors are within 5%, and the relative standard deviation within 0.30%, and these confirm that the system is highly accurate and precise in this implementation. Carrot is chosen as the target object for practical evaluation, for which the electrical impedance spectrum is a circular arc with its circular center below the real axis. Results for carrot are presented.     

Hyperspectral detection of rice damaged by rice leaf folder (Cnaphalocrocis medinalis)

The reflectance from rice (Oryza sativa L.) leaves and canopy damaged by rice leaf folder (RLF), Cnaphalocrocis medinalis (Guenée) was studied at the booting stage in order to establish a monitoring method for RLF based on hyperspectral data. The results showed that reflectance from rice leaves significantly decreased in the green (530–570 nm) and near infrared (700–1000 nm) regions, and significantly increased in the blue (450–520 nm) and red (580–700 nm) regions as the leaf-roll rate of rice increased. Reflectance from rice canopy significantly decreased in the spectral regions from 737 to 1000 nm as the infestation scale of RLF increased, and the most correlation appeared at 938 nm. Seven spectral regions 503–521, 526–545, 550–568, 581–606, 688–699, 703–715, and 722–770 nm at leaf-level, and one region 747–754 nm at canopy-level were found to be sensitive bands to exhibit the damage severity in rice by RLF. The position of the red edge peak remarkably moved to blue region, and the amplitude and area of the red edge significantly decreased when rice leaves were severely infected by RLF. Thirty-eight spectral indices at leaf-level and 29 indices at canopy-level were found to be sensitive to leaf-roll rate and infestation scale in rice, respectively. The linear regression models were built to detect the leaf-roll rate (0.0–1.0) and infestation scale (0–5) in rice using leaf- and canopy-level reflectance data. The root mean square error of the model was only 0.059 and 0.22 for the leaf-roll rate and infestation scale, respectively. These results suggested that the hyperspectral reflectance was potential to detect RLF damage severity in rice.     

Automated system developed to control pH and concentration of nutrient solution evaluated in hydroponic lettuce production

Lettuce is one of the most widely consumed leaf vegetables. In hydroponic the growth depends upon the composition of nutrient solution. Due to its nutrient absorption, the conductivity and pH suffer continuous variations. This paper describes the development of a system completely managed by a lab-made software. It monitors the conductivity and pH throughout 24 h during the whole cycle of production. Also, allows adjust automatically any variation, through solenoid valves which dispense solutions of acid/base or nutrient. The efficiency of the proposed instrumentation was evaluated by simultaneously cultivation of same kind of lettuce (Vanda) in two different ways, hydroponics in greenhouse controlled with the developed devices, and grown conventionally in soil, adopted as referential. Agronomic and chemical parameters of commercial interest were analyzed for both crop, attesting the precocity in harvest (64 against 71 days) with reduced labor, better control and higher productivity, especially in fresh and dry matter of aerial parts, presenting 267.56 and 13.33 g plant⁻¹ respectively, using the developed system. The data sequence regarding the concentration of nutrients for the automated hydroponic system was similar to those obtained by the mentioned researchers, as follows: K > N > Ca > P > Mg > S > Fe > Zn > Mn > Cu. This similarity highlights the efficiency of controlling the parameters of conductivity and pH in the instrumental system applied to hydroponics, offering the producer an effective and viable alternative in the production of lettuce.     

Modeling of sows diurnal activity pattern and detection of parturition using acceleration measurements

This article suggests and assesses two different monitoring methods for detecting sows parturition using series of three-dimensions acceleration measurements previously classified into activity types. Two groups of sows are monitored: a first group (n = 9) provided with straw (S), and a second group (n = 10) where no straw is provided (NS); two types of activity are taken into account: high active behaviour (corresponding to feeding, rooting and nest building behaviours) and total active behaviour (including any active activity type). The first method suggests modeling sows’ diurnal pattern of activity using a saw-tooth function for the probability of being active and monitoring the series using a Dynamic Generalized Linear Model (DGLM). The second method is based on a cumulative sum of hourly differences of activity, from day-to-day. Both methods use a threshold value, optimized for each group, to detect the onset of farrowing. Best results in terms of sensitivity and specificity are observed for the cumulative sum method, using individual variance and monitoring high active (sensitivity = 100%; specificity = 100%) and total active behaviours (sensitivity = 100%; specificity = 95%). Results of the DGLM method indicate a sensitivity of 100% and a specificity of 89% in average for both group S and NS. Observing the occurrence of alarm times, the DGLM method allows (i) earlier detection of farrowing: 15 h before the onset of farrowing, for both groups, as compared to 9–12 for the other methods; and (ii) a better distribution of alarms, i.e. minimize the number of alarms occurring within the last 6 h before farrowing.     

Using vegetation index and modified derivative for early detection of soybean plant injury from glyphosate

Glyphosate is a non-selective, systemic herbicide highly toxic to sensitive plant species. Its use has seen a significant increase due to the increased adoption of genetically modified glyphosate-resistant crops since the mid-1990s. Glyphosate application for weed control in glyphosate-resistant crops can drift onto an off-target area, causing unwanted injury to non-glyphosate resistant plants. Thus, early detection of crop injury from off-target drift of herbicide is critical in crop production. In non-glyphosate-resistant plants, glyphosate causes a reduction in chlorophyll content and metabolic disturbances. These subtle changes may be detectable by plant reflectance, which suggests the possibility of using optical remote sensing for early detection of drift damage to plants. In order to determine the feasibility of using optical remote sensing, a greenhouse study was initiated to measure the canopy reflectance of soybean plants using a portable hyperspectral image sensor. Non-glyphosate resistant soybean (Glycine max L. Merr.) plants were treated with glyphosate using a pneumatic track sprayer in a spray chamber. The three treatment groups were control (0 kg ae/ha), low dosage (0.086 kg ae/ha), and high dosage (0.86 kg ae/ha), each with four 2-plant pots. Hyperspectral images were taken at 4, 24, 48, and 72 h after application. The extracted canopy reflectance data was analyzed with vegetation indices. The results indicated that a number of vegetation indices could identify crop injury at 24 h after application, at which time visual inspection could not distinguish between glyphosate injured and non-treated plants. To improve the results a modified method of spectral derivative analysis was proposed and applied to find that the method produced better results than the vegetation indices. Four selected first derivatives at wavelength 519, 670, 685, and 697 nm could potentially differentiate crop injury at 4 h after treatment. The overall false positive rate was lower than the vegetation indices. Furthermore, the derivatives demonstrated the ability to separate treatment groups with different dosages. The study showed that hyperspectral imaging of plant canopy reflectance could be a useful tool for early detection of soybean crop injury from glyphosate, and that the modified spectral derivative analysis had a better performance than vegetation indices.     

A high quality low-cost digital microscope minirhizotron system

An easily reproducible system is presented for minirhizotron image acquisition based on a digital microscope and entirely built with low-cost components. The system weighs 2.28 kg, is connected to a portable computer through USB, and allows collection and storage of high-quality digital images without other components or power sources. Settings at 25× magnification provide pixel sizes of 25 and 12 μm respectively with a medium pixel density microscope (640 × 480 pixel), and a high pixel density microscope (1280 × 1024 pixel). This kind of system coupled with recent developments of robotics and automatic image analysis for minirhizotron frames allows to envisage fast, low-labour and low-cost root investigation methods with a high degree of automation.     

Model fusion for prediction of apple firmness using hyperspectral scattering image

Hyperspectral scattering image is an advanced technology widely used in non-destructive measurement of fruit quality. To develop a better prediction model for apple firmness, the present study investigates a model fusion method coupled with wavelength selection algorithms. The current paper first discusses two wavelength selection algorithms, namely, uninformative variable elimination (UVE) and supervised affinity propagation (SAP). The selected effective wavelengths are then set as input to the partial least square (PLS) model. Six hundred “Golden Delicious” apples were analyzed. The first 450 apples were used as sample for the calibration model, whereas the remaining 150 were used for the prediction model. Compared with full wavelengths, the number of effective wavelengths based on the UVE and SAP algorithms decreased to 34% and 35%, but the correlation coefficient of prediction (Rp) increased from 0.791 to 0.805 and 0.814, whereas the root mean-square error of prediction (RMSEP) decreased from 6.00 to 5.73 and 5.71 N, respectively. A fusion model was then developed using UVE-PLS and SAP-PLS models coupled with backpropagation neural network. A better prediction accuracy was achieved from the fusion model (Rp = 0.828 and RMSEP = 5.53 N). The model fusion provides an effective modeling method for apple firmness prediction using hyperspectral scattering image technique.     

Development approach and evaluation of the Nutrient Expert software for nutrient management in cereal crops

Meeting the demand for more food in the next 20–30 years requires intensifying cereal cropping systems and increasing current yields to about 70–80% of the genetic yield potential. A dynamic and robust nutrient management approach such as site-specific nutrient management (SSNM) will be essential to increase yields and optimize profits while maintaining the productivity of these intensive cropping systems. SSNM has increased yield and profit in rice, maize, and wheat in major cropping systems in Asia; but, crop advisors have found it complex and difficult to implement in the field. Nutrient Expert (NE) was developed to provide crop advisors with a simpler and faster way to use SSNM. NE enables crop advisors to develop SSNM recommendations using existing site information. Nutrient Expert for Hybrid Maize (NEHM) increased yield and profit of farmers in Indonesia and the Philippines. In Indonesia, NEHM increased yield by 0.9 t ha⁻¹, which increased profit by US$ 270 ha⁻¹ over farmer’s fertilizer practice (FFP). Compared with FFP, NEHM recommendations reduced fertilizer P (−4 kg ha⁻¹), increased fertilizer K (+11 kg ha⁻¹), and did not significantly change fertilizer N. In the Philippines, NEHM increased yield by 1.6 t ha⁻¹ and profit by US$ 379 ha⁻¹ compared with FFP. Compared with FFP, NEHM gave higher rates of all three nutrients (+25 kg N ha⁻¹, +4 kg P ha⁻¹, and +11 kg K ha⁻¹), which substantially increased fertilizer costs (US$ 64 ha⁻¹) but still increased profit by about six times the additional investment in fertilizer. NE accounts for the important factors affecting site-specific recommendations, which makes it a suitable starting point for developing nutrient management tools to reach more users.     

Development and evaluation of a novel system for monitoring harvest labor efficiency

This paper introduces a real-time labor monitoring system (LMS) with the ability to track and record individual picker efficiency during manual harvest of specialty crops. This system utilizes existing commercial harvest equipment and integrates a digital weighing scale, RFID reader, computational unit, and a portable datalogger carried by pickers. The RFID reader, digital scale and computational unit are assembled on a common portable chassis. As pickers transfer fruit into a standard collection bin, the system reads the picker’s ID (RFID tag) and the weight of fruit. Weight data can then be transmitted wirelessly to the picker’s datalogger which records and displays the incremental and total weight of harvested fruit. An algorithm was developed in Matlab® to record, process and store the data, as well as to transmit wirelessly the weight value to the wearable datalogger. System prototypes were assembled and field-tested for accuracy and reliability during commercial harvest of sweet cherries (Prunus avium L.) in the Pacific Northwest. The LMS reliably calculated the harvest rate, picking cost, weight of harvested fruit, number of harvested buckets, range in fruit weight per bucket, and mean fruit weight per bucket, in real time. The mean harvest rate (±standard error) in a ‘Chelan’/Mazzard sweet cherry orchard trained to a steep leader architecture was 0.53 ± 0.13 kg/person/min. Harvest rate was similar for the same genotype trained to a steep leader (3 leaders) at 0.50 ± 0.10 kg/person/min and for harvest of ‘Tieton’/‘Gisela®6’ trees trained to a central leader architecture (0.53 ± 0.15 kg/person/min). When surveyed after using the LMS, every picker indicated a preference for knowing the exact weight of fruit they harvested compared with the current system of reimbursement per bucket or bin. Using the LMS, reliable data on worker efficiency can be collected with minimal interference with standard commercial practices.     

Kinematic correction for a spatial offset between sensor and position data in on-the-go sensor applications

On-the-go data collection is the basis for many applications that require spatially referenced information at a sub-field scale. A spatial or temporal offset can occur between sensor and position measurements (mostly obtained with global navigation satellite systems such as the Global Positioning System, GPS), e.g. if the antenna of the navigation system cannot be placed on top of the sensor. To correct for such an offset, we present a kinematic model that accounts for the mechanisms underlying the offset. Our model considers a sensor on a cart or sledge towed by a drawing vehicle equipped with a positioning system. The model was applied to a soil electrical conductivity survey on a 135 ha site with over 100 000 measured positions. Application of the model halved a nugget effect in semivariograms, significantly (p < 0.01) reduced prediction errors and thus substantially improved map quality. The model allows offset correction between sensors and a positioning system mounted independently on agricultural machinery, even retrospectively from the geometric configuration of the positioning system-sensor combination. Thus we propose the model for use in research and practical applications of sensor based mapping.     

Nondestructive measurement of internal quality of Nanfeng mandarin fruit by charge coupled device near infrared spectroscopy

The soluble solids content (SSC) and total acidity (TA) are the major characteristics for assessing quality and maturity of Nanfeng mandarin fruits. The feasibility of charge coupled device near infrared spectroscopy (CCD-NIRS) combining with effective wavelengths selection algorithm used to measure SSC and TA nondestructively was investigated. The effective wavelengths to SSC and TA were chosen by interval partial least squares (iPLS) in the wavelength range of 600–980 nm. The predictive ability of SSC model used PLS regression was improved with r of 0.92 and RMSEP of 0.65 °Brix using effective wavelengths of 681.36–740.51 nm, 798.60–836.19 nm and 945.52–962.75 nm. The TA model was simplified with r of 0.64 and RMSEP of 0.09% using effective wavelengths of 817.57–836.19 nm, 909.85–927.60 nm and 945.52–962.75 nm. The experimental results demonstrated that the CCD-NIRS technique combining with iPLS algorithm was a feasible method to measure SSC and TA of Nanfeng mandarin fruits nondestructively.     

Spatial multivariate classification of an arable field into compact management zones based on past crop yields

Many farmers want to take advantage of modern technology to control fertilizer applications in large fields more precisely in smaller zones. We have investigated zonation on past yields by three forms of k-means clustering with geostatistical smoothing and compared the outcomes with yield response to added nitrogen. We had data on wheat yield for three years on 2187 10 × 10 m squares in a 29-ha field in the English midlands. Two of the methods grouped the squares by a ‘hard’ means technique, the first used their similarities which had been modified with their spatial covariance function and the second used their dissimilarities which had been modified with their variogram. The third method computed their memberships to fuzzy classes and smoothed the memberships with the variogram for final classification. The resulting spatially smoothed classifications were evaluated by the extent to which they responded differentially to applied nitrogen in a fourth year.The best compromise between spatial coherence and minimum variance seemed to be the smoothed fuzzy classification; it created reasonably compact zones that a well-equipped farmer might be able to manage. However, the nitrogen response functions were parallel, and it is unlikely that a farmer would benefit financially by fertilizing the zones separately. The soil map of the field also distinguished coherent compact zones, and these differed in the forms of the responses of their crops to fertilizer nitrogen. For this field, it might be more profitable to manage the nitrogen application according to soil type.     

Acceleration of CT reconstruction for wheat tiller inspection based on adaptive minimum enclosing rectangle

Tiller number is highly correlated with grain yield in wheat. Traditional observation of wheat tiller number is still manual. Previously, our group developed a high-throughput system for measuring automatically rice tillers (H-SMART) based on X-ray computed tomography (CT), providing high accuracy for measuring rice tillers. However, the time-consuming reconstruction, which is necessary to generate tomographic images, limits the throughput improvement of system as well as the CT potential for the real-time applications. In order to accelerate the reconstruction process, we present an adaptive minimum enclosing rectangle (AMER) method to reduce the number of reconstructed pixels from the full field of view (FOV) and apply parallel processing using Graphics Processing Unit (GPU). The reconstruction time and speedup with different methods were discussed. Compared to the AMER method, GPU technique improved reconstruction with a higher speedup of approximately 200 times. And the speedup with AMER method was determined by two factors: area ratio of AMER and FOV, and the longest distance between the vertices of the AMER and the rotation center. Besides reconstruction, tiller identification could also be accelerated by AMER. Moreover, the tiller measurement accuracy did not decrease. With the combination of AMER and GPU, the entire tiller inspection time for a pot-grown plant was reduced from about 11870 ms to less than 200 ms. In sum, the optimized method met the requirement of real-time imaging and expanded CT application in plant phenomics and agriculture photonics.     

Chinese Oil Plants Information System (COPIS): An on-line information store,query and management system for three chinese industrial oil plants

Researches on Cornus wilsoniana, Ricinus communis, and Symplocos paniculata have generated a large amount of data. In order to assist researchers to store and exchange data efficiently, an on-line interactive platform, called Chinese Oil Plants Information System (COPIS), was developed. The authors collected and organized the data of three Chinese industrial oil plants. The system applied AMP (Apache + PHP + Mysql) Web frame based on the standard Browser/Server of three-layer structure. COPIS has realized data query and real-time update, as well as history records for future tracking. Additionally, it provides several analytical tools for C. wilsoniana cultivars. This paper describes the structure and content of COPIS, explains functions of the database, introduces several analytical tools and methods through case study, and briefly evaluates the usage of COPIS.     

Agricultural applications of a low-cost infrared thermometer

Plant canopy temperature is used in many studies of plant/environment interactions and non-contact measurement is often made with radiometric surface thermometers commonly referred to as infrared thermometers. Industrial-quality infrared thermocouples are widely available and often used in agricultural research. While research on canopy temperature has provided management tools for production agriculture, the high cost of the industrial-quality infrared thermocouples has limited their adoption and use in production agriculture settings. Our objective was to evaluate a low-cost consumer-quality infrared thermocouple as a component of a wireless thermal monitoring system designed for use in a production agriculture setting. The performances of industrial-quality and low-cost consumer-quality sensors were compared under controlled constant temperature and under field conditions using both grass and cotton canopies. Results demonstrate that under controlled constant-temperature the two types of infrared thermocouples were “significantly the same” at 10 °C, 20 °C and 30 °C and “significantly not the same” at 40 °C and 50 °C. Across the temperature range tested, the consumer-quality infrared thermocouples temperature reading was closer to the thermocouple reading than the industrial-quality infrared thermocouples. A field comparison of industrial-quality and consumer-quality infrared thermocouple sensors monitoring a grass canopy and a cotton canopy indicated that the two types of sensors were similar over a 13–35 °C range. The measurement of temperature made with two types of sensors would not differ significantly. Based on these results we conclude that the lower-cost consumer-quality infrared thermometers are suitable for use in production agricultural applications.     

Predicting drift from field spraying by means of a 3D computational fluid dynamics model

In order to investigate and understand drift from field sprayers, a steady state computational fluid dynamics (CFD) model was developed. The model was developed in 3D in order to increase the understanding of the causes of drift: a deviation in the wind direction cannot be captured by a 2D approach, the wake behind a wind screen is not symmetrical, the effects of a changed nozzle orientation may not be symmetrical. The model's accuracy was validated with field experiments carried out according to the international standard ISO 22866. A field sprayer with a spray boom width of 27 m and 54 nozzles (Hardi ISO F110-03 at 3 bar) was driving at 2.22 m/s over a flat pasture. During the experiments the wind direction was perpendicular to the tractor track. The model explained the variation in drift replicates during each single field experiment through varying boom height (0.3–0.7 m), wind velocity (1.3–2.5 m/s), wind deviation (−18° to +18°) from the direction perpendicular to the tractor track and injection velocity of the droplets (17–27 m/s). Boom movements had the highest impact on the variations in drift values (deviations in drift deposits of 25%), followed by variation in wind velocity (deviations in drift deposits of 3%) and injection velocity of the droplets (deviations in drift deposits of 2.5%). Wind deviation from the direction perpendicular to the tractor track had a reducing effect on the drift values (deviations in drift deposits of 2%). Small variations in driving speed had little influence on drift values. Near drift (<5 m) is predicted well by the model but the increased complexity compromised the predictions at greater distances. The model will be further developed in order to improve far drift prediction. Dynamic simulations will be performed and the model for turbulent dispersion will be optimized. The model did not require calibration.